Traumatic brain injuries (TBI) can impair cognitive functioning long after recovery from the initial trauma. The rehabilitation of Veterans with TBI is a critical need for the VA. The ability to understand and predict patient response to rehabilitation interventions will better serve our Veterans and benefit the VA health care system. The primary objective of this project is to develop neural markers that are sensitive to changes in brain function that occur with neurocognitive skills training. There is significant variability in response to any given rehabilitation intervention, and the neural basis of this variability is poorly understood. Information about the brain mechanisms that underlie learning and plasticity after brain injury will help guide the development of novel interventions. Electroencephalography (EEG) may provide markers useful for predicting and understanding improvements in brain functioning with rehabilitation. Our training approach emphasizes improving skills of goal-directed self-regulation, and the application of these skills across multiple challenge contexts. Digital game training scenarios increase the intensity and range of skill application experience. Trainees practice these skills in daily life and in relation to personal goals. This project will investigate predictive markers and plastic changes that reflect functional improvements using EEG recordings. EEG will provide a real time neural signal to track the deployment of top-down control functions and model brain network connectivity patterns while cognitive processes are engaged. We will examine potential predictors of behavioral responses to training using well- validated event-related potential (ERP) components, and novel application of EEG network connectivity measures. The proposed studies will: (1) test whether changes in EEG markers can explain variability in behavioral responses to training, and (2) evaluate whether specific EEG markers at baseline can predict subsequent patient response to training. To determine the specificity of findings from patients in training, comparison patients not undergoing the intervention will be tested at two time points. EEG will be recorded while participants are engaged in a demanding working memory task with distraction, both before and after training. We will assess whether specific markers (the ERP old/new effect during memory retrieval, and theta band activity during encoding and maintenance) will change with training. We will also assess whether EEG changes can explain variability in response to training as measured by scores on well-validated tests of working memory span and attention/executive function. In addition, we will record EEG while participants are engaged in a focused-rest state before and after training. Network connectivity dynamics will be calculated using a graph theoretic approach. The use of innovative, data-driven methods will maximize our chances of identifying an appropriate predictive biomarker. The project will address the health care needs of Veterans by addressing outstanding questions regarding the neural bases of improvements in cognitive functioning, with the potential to identify biomarkers useful for predicting patient responses to training.